Unsupervised Segmentation of Choroidal Neovascularization for Optical Coherence Tomography Angiography by Grid Tissue-Like Membrane Systems

Xue, Jie; Yan, Shuo; Wang, Yuan; Liu, Tingting; Feng, Qinghua; Zhang, Hongyan; Qiu, Chenggong; Qu, Jianhua; Liu, Xiyu; Li, Dengwang

doi:10.1109/access.2019.2943186

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…In the case of OCTA image segmentation, the majority of the analyzed studies used pixel intensity as a way to group together objects, using common methods such as k-means clustering [63][64][65], or other clustering algorithms such as fuzzy c-means clustering [66] and a modified CLIQUE clustering technique [67]. An interesting study that used local features for clustering and not pixel intensity is a method by Engberg et al [68] which was based on building a dictionary using pre-annotated data and then processing the unseen images by computing the similarity/dissimilarity.…”

Section: Clusteringmentioning

confidence: 99%

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Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

et al. 2021

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Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning.

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Section: Clusteringmentioning

confidence: 99%

“…On this image, the DSC was equal to 0.82 for larger vessels and 0.71 for capillaries. For the CNV/Choriocapillaris application, the study by Xue et al [67] had a final DSC equal to 0.84.…”

Section: Clusteringmentioning

confidence: 99%

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

et al. 2021

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“…In recent years, researchers have turned to the application of membrane computing models. Generally, there are three main families: cell-like P system [30], tissue-like P system [31,32] and neural-like P system [33]. The structure of the tissue-like P system can be viewed as a net, a tissue-like membrane system of degree m > 0 is constructed as follows [34]:…”

Section: Tissue-like and Cell-like Membrane Systemsmentioning

confidence: 99%

Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection

2020

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Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a robust and superior performance than the state-of-the-art methods and improved the average on three used indicators significantly.

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“…Most of the work focused on CNV lesion region segmentation [12][13][14] and retinal layers segmentation 15,16 in the past. Accurately segmenting the lesion area of CNV can quantify the area, volume, width, height, optical density value, and other properties of CNV.…”

Section: Introductionmentioning

confidence: 99%

Morphological prognosis prediction of choroid neovascularization from longitudinal SD‐OCT images

et al. 2023

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BackgroundChoroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment.PurposeAccurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment.MethodsIn this paper, we develop a CNN‐based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD‐OCT) B‐scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co‐segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow‐up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder‐recurrent‐decoder framework is developed for CNV growth prediction, in which an attention‐based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations.ResultsThe registration network rigidly registers the follow‐up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state‐of‐the‐art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow‐up visit.ConclusionThe proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.

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Unsupervised Segmentation of Choroidal Neovascularization for Optical Coherence Tomography Angiography by Grid Tissue-Like Membrane Systems

Cited by 7 publications

References 32 publications

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection

Morphological prognosis prediction of choroid neovascularization from longitudinal SD‐OCT images

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